Parse Fitbit summary and intraday data; Extract Fitbit daily features from summary data
Meng Li
parent
d07bb9ed5f
commit
915bdd04b1
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@ -59,15 +59,19 @@ rule all:
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expand("data/processed/{pid}/applications_foreground_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"]),
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expand("data/raw/{pid}/fitbit_{fitbit_sensor}_with_datetime.csv",
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expand("data/raw/{pid}/fitbit_{fitbit_sensor}_{fitbit_data_type}_with_datetime.csv",
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pid=config["PIDS"],
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fitbit_sensor=config["FITBIT_SENSORS"]),
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fitbit_sensor=config["FITBIT_SENSORS"],
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fitbit_data_type=config["FITBIT_DATA_TYPE"]),
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expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["HEARTRATE"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/fitbit_step_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["STEP"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["SLEEP"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/wifi_{segment}.csv",
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pid=config["PIDS"],
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segment = config["WIFI"]["DAY_SEGMENTS"]),
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@ -3,6 +3,7 @@ SENSORS: [applications_crashes, applications_foreground, applications_notificati
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FITBIT_TABLE: [fitbit_data]
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FITBIT_SENSORS: [heartrate, steps, sleep, calories]
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FITBIT_DATA_TYPE: [summary, intraday]
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# Participants to include in the analysis
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# You must create a file for each participant
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@ -114,6 +115,7 @@ APPLICATIONS_FOREGROUND:
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HEARTRATE:
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DAY_SEGMENTS: *day_segments
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FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "lengthoutofrange", "lengthfatburn", "lengthcardio", "lengthpeak"]
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DAILY_FEATURES_FROM_SUMMARY_DATA: ["restinghr"] # calories related features might be inaccurate: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"]
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STEP:
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DAY_SEGMENTS: *day_segments
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@ -124,6 +126,11 @@ STEP:
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THRESHOLD_ACTIVE_BOUT: 10 # steps
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INCLUDE_ZERO_STEP_ROWS: True
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SLEEP:
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DAY_SEGMENTS: *day_segments
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SLEEP_TYPES: ["main", "nap", "all"]
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DAILY_FEATURES_FROM_SUMMARY_DATA: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"]
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WIFI:
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DAY_SEGMENTS: *day_segments
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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@ -133,7 +140,7 @@ PARAMS_FOR_ANALYSIS:
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SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"]
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DAY_SEGMENTS: *day_segments
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PHONE_FEATURES: [accelerometer, applications_foreground, battery, call_incoming, call_missed, call_outgoing, activity_recognition, light, location_barnett, screen, sms_received, sms_sent]
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FITBIT_FEATURES: [fitbit_heartrate, fitbit_step]
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FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep]
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PHONE_FITBIT_FEATURES: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
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DEMOGRAPHIC_FEATURES: [age, gender, inpatientdays]
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CATEGORICAL_DEMOGRAPHIC_FEATURES: ["gender"]
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@ -167,12 +167,25 @@ rule applications_foreground_features:
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script:
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"../src/features/applications_foreground_features.py"
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rule wifi_features:
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input:
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"data/raw/{pid}/wifi_with_datetime.csv"
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params:
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day_segment = "{day_segment}",
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features = config["WIFI"]["FEATURES"]
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output:
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"data/processed/{pid}/wifi_{day_segment}.csv"
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script:
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"../src/features/wifi_features.R"
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rule fitbit_heartrate_features:
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input:
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"data/raw/{pid}/fitbit_heartrate_with_datetime.csv",
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heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv",
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heartrate_intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv"
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params:
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day_segment = "{day_segment}",
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features = config["HEARTRATE"]["FEATURES"],
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daily_features_from_summary_data = config["HEARTRATE"]["DAILY_FEATURES_FROM_SUMMARY_DATA"]
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output:
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"data/processed/{pid}/fitbit_heartrate_{day_segment}.csv"
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script:
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@ -193,13 +206,15 @@ rule fitbit_step_features:
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script:
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"../src/features/fitbit_step_features.py"
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rule wifi_features:
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input:
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"data/raw/{pid}/wifi_with_datetime.csv"
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rule fitbit_sleep_features:
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input:
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sleep_summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv",
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sleep_intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv"
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params:
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day_segment = "{day_segment}",
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features = config["WIFI"]["FEATURES"]
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sleep_types = config["SLEEP"]["SLEEP_TYPES"],
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daily_features_from_summary_data = config["SLEEP"]["DAILY_FEATURES_FROM_SUMMARY_DATA"]
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output:
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"data/processed/{pid}/wifi_{day_segment}.csv"
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"data/processed/{pid}/fitbit_sleep_{day_segment}.csv"
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script:
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"../src/features/wifi_features.R"
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"../src/features/fitbit_sleep_features.py"
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@ -99,7 +99,8 @@ rule fitbit_with_datetime:
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local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"],
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fitbit_sensor = "{fitbit_sensor}"
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output:
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"data/raw/{pid}/fitbit_{fitbit_sensor}_with_datetime.csv"
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summary_data = "data/raw/{pid}/fitbit_{fitbit_sensor}_summary_with_datetime.csv",
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intraday_data = "data/raw/{pid}/fitbit_{fitbit_sensor}_intraday_with_datetime.csv"
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script:
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"../src/data/fitbit_readable_datetime.py"
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@ -0,0 +1,35 @@
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import json
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import pandas as pd
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from datetime import datetime
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CALORIES_INTRADAY_COLUMNS = ("device_id",
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"level", "mets", "value",
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"local_date_time", "local_date", "local_month", "local_day",
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"local_day_of_week", "local_time", "local_hour", "local_minute",
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"local_day_segment")
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def parseCaloriesData(calories_data, HOUR2EPOCH):
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if calories_data.empty:
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return pd.DataFrame(), pd.DataFrame(columns=CALORIES_INTRADAY_COLUMNS)
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device_id = calories_data["device_id"].iloc[0]
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records_intraday = []
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# Parse JSON into individual records
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for record in calories_data.fitbit_data:
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record = json.loads(record) # Parse text into JSON
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curr_date = datetime.strptime(
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record["activities-calories"][0]["dateTime"], "%Y-%m-%d")
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dataset = record["activities-calories-intraday"]["dataset"]
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for data in dataset:
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d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
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d_datetime = datetime.combine(curr_date, d_time)
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row_intraday = (device_id,
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data["level"], data["mets"], data["value"],
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d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day,
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d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute,
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HOUR2EPOCH[d_datetime.hour])
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records_intraday.append(row_intraday)
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return pd.DataFrame(), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS)
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@ -0,0 +1,114 @@
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import json
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import pandas as pd
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from datetime import datetime
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HR_SUMMARY_COLUMNS = ("device_id",
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"local_date",
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"heartrate_daily_restinghr",
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"heartrate_daily_caloriesoutofrange",
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"heartrate_daily_caloriesfatburn",
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"heartrate_daily_caloriescardio",
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"heartrate_daily_caloriespeak")
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HR_INTRADAY_COLUMNS = ("device_id",
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"heartrate", "heartrate_zone",
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"local_date_time", "local_date", "local_month", "local_day",
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"local_day_of_week", "local_time", "local_hour", "local_minute",
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"local_day_segment")
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def parseHeartrateZones(heartrate_data):
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# Get the range of heartrate zones: outofrange, fatburn, cardio, peak
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# refer to: https://help.fitbit.com/articles/en_US/Help_article/1565
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heartrate_fitbit_data = json.loads(heartrate_data["fitbit_data"].iloc[0])["activities-heart"][0]
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# API Version X: not sure the exact version
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if "heartRateZones" in heartrate_fitbit_data:
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heartrate_zones = heartrate_fitbit_data["heartRateZones"]
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# API VERSION Y: not sure the exact version
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elif "value" in heartrate_fitbit_data:
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heartrate_zones = heartrate_fitbit_data["value"]["heartRateZones"]
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else:
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raise ValueError("Heartrate zone are stored in an unkown format, this could mean Fitbit's heartrate API changed")
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heartrate_zones_range = {}
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for hrzone in heartrate_zones:
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heartrate_zones_range[hrzone["name"].lower().replace(" ", "")] = [hrzone["min"], hrzone["max"]]
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return heartrate_zones_range
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def parseHeartrateSummaryData(record_summary, device_id, curr_date):
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# API Version X: not sure the exact version
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if "heartRateZones" in record_summary:
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heartrate_zones = record_summary["heartRateZones"]
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d_resting_heartrate = record_summary["value"] if "value" in record_summary else None
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# API VERSION Y: not sure the exact version
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elif "value" in record_summary:
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heartrate_zones = record_summary["value"]["heartRateZones"]
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d_resting_heartrate = record_summary["value"]["restingHeartRate"] if "restingHeartRate" in record_summary["value"] else None
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else:
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ValueError("Heartrate zone are stored in an unkown format, this could mean Fitbit's heartrate API changed")
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if "caloriesOut" in heartrate_zones[0]:
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d_calories_outofrange = heartrate_zones[0]["caloriesOut"]
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d_calories_fatburn = heartrate_zones[1]["caloriesOut"]
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d_calories_cardio = heartrate_zones[2]["caloriesOut"]
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d_calories_peak = heartrate_zones[3]["caloriesOut"]
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else:
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d_calories_outofrange, d_calories_fatburn, d_calories_cardio, d_calories_peak = None, None, None, None
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row_summary = (device_id,
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curr_date,
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d_resting_heartrate,
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d_calories_outofrange,
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d_calories_fatburn,
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d_calories_cardio,
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d_calories_peak)
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return row_summary
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def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range, HOUR2EPOCH):
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for data in dataset:
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d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
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d_datetime = datetime.combine(curr_date, d_time)
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d_hr = data["value"]
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# Get heartrate zone by range: min <= heartrate < max
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d_hrzone = None
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for hrzone, hrrange in heartrate_zones_range.items():
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if d_hr >= hrrange[0] and d_hr < hrrange[1]:
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d_hrzone = hrzone
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break
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row_intraday = (device_id,
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d_hr, d_hrzone,
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d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day,
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d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute,
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HOUR2EPOCH[d_datetime.hour])
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records_intraday.append(row_intraday)
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return records_intraday
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def parseHeartrateData(heartrate_data, HOUR2EPOCH):
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if heartrate_data.empty:
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return pd.DataFrame(columns=HR_COLUMNS)
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device_id = heartrate_data["device_id"].iloc[0]
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records_summary, records_intraday = [], []
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heartrate_zones_range = parseHeartrateZones(heartrate_data)
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# Parse JSON into individual records
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for record in heartrate_data.fitbit_data:
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record = json.loads(record) # Parse text into JSON
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curr_date = datetime.strptime(record["activities-heart"][0]["dateTime"], "%Y-%m-%d")
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record_summary = record["activities-heart"][0]
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row_summary = parseHeartrateSummaryData(record_summary, device_id, curr_date)
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records_summary.append(row_summary)
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dataset = record["activities-heart-intraday"]["dataset"]
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records_intraday = parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range, HOUR2EPOCH)
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return pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS)
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@ -0,0 +1,109 @@
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import json
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import pandas as pd
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from datetime import datetime
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SLEEP_CODE2LEVEL = ["asleep", "restless", "awake"]
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SLEEP_SUMMARY_COLUMNS_V1_2 = ("device_id", "efficiency",
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"minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed",
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"is_main_sleep", "type",
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"local_start_date_time", "local_end_date_time",
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"local_start_date", "local_end_date",
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"local_start_day_segment", "local_end_day_segment")
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SLEEP_SUMMARY_COLUMNS_V1 = SLEEP_SUMMARY_COLUMNS_V1_2 + ("count_awake", "duration_awake", "count_awakenings", "count_restless", "duration_restless")
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SLEEP_INTRADAY_COLUMNS = ("device_id",
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# For "classic" type, original_level is one of {"awake", "restless", "asleep"}
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# For "stages" type, original_level is one of {"wake", "deep", "light", "rem"}
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"original_level",
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# For "classic" type, unified_level is one of {0, 1} where 0: awake {"awake" + "restless"}, 1: asleep {"asleep"}
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# For "stages" type, unified_level is one of {0, 1} where 0: awake {"wake"}, 1: asleep {"deep" + "light" + "rem"}
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"unified_level",
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# one of {0, 1} where 0: nap, 1: main sleep
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"is_main_sleep",
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# one of {"classic", "stages"}
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"type",
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"local_date_time", "local_date", "local_month", "local_day",
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"local_day_of_week", "local_time", "local_hour", "local_minute",
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"local_day_segment")
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# Parse one record for sleep API version 1
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def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH):
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# Summary data
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sleep_record_type = "classic"
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d_start_datetime = datetime.strptime(record["startTime"][:18], "%Y-%m-%dT%H:%M:%S")
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d_end_datetime = datetime.strptime(record["endTime"][:18], "%Y-%m-%dT%H:%M:%S")
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row_summary = (device_id, record["efficiency"],
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record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"],
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d_is_main_sleep, sleep_record_type,
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d_start_datetime, d_end_datetime,
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d_start_datetime.date(), d_end_datetime.date(),
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HOUR2EPOCH[d_start_datetime.hour], HOUR2EPOCH[d_end_datetime.hour],
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record["awakeCount"], record["awakeDuration"], record["awakeningsCount"],
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record["restlessCount"], record["restlessDuration"])
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records_summary.append(row_summary)
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# Intraday data
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start_date = d_start_datetime.date()
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end_date = d_end_datetime.date()
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is_before_midnight = True
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curr_date = start_date
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for data in record["minuteData"]:
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# For overnight episodes, use end_date once we are over midnight
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d_time = datetime.strptime(data["dateTime"], '%H:%M:%S').time()
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if is_before_midnight and d_time.hour == 0:
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curr_date = end_date
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d_datetime = datetime.combine(curr_date, d_time)
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# API 1.2 stores original_level as strings, so we convert original_levels of API 1 to strings too
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# (1: "asleep", 2: "restless", 3: "awake")
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d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1]
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# unified_level summarises original_level (we came up with this classification)
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# 0 is awake, 1 is asleep
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# {"awake" + "restless"} are set to 0 and {"asleep"} is set to 1
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d_unified_level = 0 if d_original_level == "awake" or d_original_level == "restless" else 1
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row_intraday = (device_id,
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d_original_level, d_unified_level, d_is_main_sleep, sleep_record_type,
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d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day,
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d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute,
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HOUR2EPOCH[d_datetime.hour])
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records_intraday.append(row_intraday)
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return records_summary, records_intraday
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# Parse one record for sleep API version 1.2
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def parseOneRecordForV12(record, d_is_main_sleep, records_summary, records_intraday):
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return None
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def parseSleepData(sleep_data, HOUR2EPOCH):
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if sleep_data.empty:
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return pd.DataFrame(columns=SLEEP_SUMMARY_COLUMNS_V1), pd.DataFrame(columns=SLEEP_INTRADAY_COLUMNS)
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device_id = sleep_data["device_id"].iloc[0]
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records_summary, records_intraday = [], []
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# Parse JSON into individual records
|
||||
for multi_record in sleep_data.fitbit_data:
|
||||
for record in json.loads(multi_record)["sleep"]:
|
||||
# Whether the sleep episode is nap (0) or main sleep (1)
|
||||
d_is_main_sleep = 1 if record["isMainSleep"] else 0
|
||||
|
||||
# For sleep API version 1
|
||||
if "awakeCount" in record:
|
||||
SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1
|
||||
records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH)
|
||||
# For sleep API version 1.2
|
||||
else:
|
||||
SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2
|
||||
raise ValueError("Sleep data for API v1.2 is not supported yet.")
|
||||
|
||||
return pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=SLEEP_INTRADAY_COLUMNS)
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
import json
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
STEPS_INTRADAY_COLUMNS = ("device_id",
|
||||
"steps",
|
||||
"local_date_time", "local_date", "local_month", "local_day",
|
||||
"local_day_of_week", "local_time", "local_hour", "local_minute",
|
||||
"local_day_segment")
|
||||
|
||||
|
||||
def parseStepsData(steps_data, HOUR2EPOCH):
|
||||
if steps_data.empty:
|
||||
return pd.DataFrame(), pd.DataFrame(columns=STEPS_COLUMNS)
|
||||
device_id = steps_data["device_id"].iloc[0]
|
||||
records_intraday = []
|
||||
# Parse JSON into individual records
|
||||
for record in steps_data.fitbit_data:
|
||||
record = json.loads(record) # Parse text into JSON
|
||||
curr_date = datetime.strptime(
|
||||
record["activities-steps"][0]["dateTime"], "%Y-%m-%d")
|
||||
dataset = record["activities-steps-intraday"]["dataset"]
|
||||
for data in dataset:
|
||||
d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
|
||||
d_datetime = datetime.combine(curr_date, d_time)
|
||||
|
||||
row_intraday = (device_id,
|
||||
data["value"],
|
||||
d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day,
|
||||
d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute,
|
||||
HOUR2EPOCH[d_datetime.hour])
|
||||
|
||||
records_intraday.append(row_intraday)
|
||||
|
||||
return pd.DataFrame(), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS)
|
||||
|
|
@ -1,6 +1,10 @@
|
|||
import pandas as pd
|
||||
import pytz, json
|
||||
from datetime import datetime
|
||||
from fitbit_parse_sensors.fitbit_parse_heartrate import parseHeartrateData
|
||||
from fitbit_parse_sensors.fitbit_parse_sleep import parseSleepData
|
||||
from fitbit_parse_sensors.fitbit_parse_steps import parseStepsData
|
||||
from fitbit_parse_sensors.fitbit_parse_calories import parseCaloriesData
|
||||
|
||||
|
||||
NIGHT = "night"
|
||||
|
|
@ -10,30 +14,6 @@ EVENING = "evening"
|
|||
HOUR2EPOCH = [NIGHT] * 6 + [MORNING] * 6 + [AFTERNOON] * 6 + [EVENING] * 6
|
||||
|
||||
|
||||
HR_COLUMNS = ("device_id",
|
||||
"heartrate", "heartrate_zone",
|
||||
"local_date_time", "local_date", "local_month", "local_day",
|
||||
"local_day_of_week", "local_time", "local_hour", "local_minute",
|
||||
"local_day_segment")
|
||||
|
||||
SLEEP_COLUMNS = ("device_id",
|
||||
"sleep", # 1: "asleep", 2: "restless", or 3: "awake"
|
||||
"local_date_time", "local_date", "local_month", "local_day",
|
||||
"local_day_of_week", "local_time", "local_hour", "local_minute",
|
||||
"local_day_segment")
|
||||
|
||||
STEPS_COLUMNS = ("device_id",
|
||||
"steps",
|
||||
"local_date_time", "local_date", "local_month", "local_day",
|
||||
"local_day_of_week", "local_time", "local_hour", "local_minute",
|
||||
"local_day_segment")
|
||||
|
||||
CALORIES_COLUMNS = ("device_id",
|
||||
"level", "mets", "value",
|
||||
"local_date_time", "local_date", "local_month", "local_day",
|
||||
"local_day_of_week", "local_time", "local_hour", "local_minute",
|
||||
"local_day_segment")
|
||||
|
||||
def drop_duplicates(data, local_timezone):
|
||||
"""
|
||||
Data is pulled in intraday manner. Since data will be duplicated until the
|
||||
|
|
@ -47,160 +27,6 @@ def drop_duplicates(data, local_timezone):
|
|||
|
||||
return data
|
||||
|
||||
def parse_steps_data(steps_data):
|
||||
if steps_data.empty:
|
||||
return pd.DataFrame(columns=STEPS_COLUMNS)
|
||||
device_id = steps_data["device_id"].iloc[0]
|
||||
records = []
|
||||
# Parse JSON into individual records
|
||||
for record in steps_data.fitbit_data:
|
||||
record = json.loads(record) # Parse text into JSON
|
||||
curr_date = datetime.strptime(
|
||||
record["activities-steps"][0]["dateTime"], "%Y-%m-%d")
|
||||
dataset = record["activities-steps-intraday"]["dataset"]
|
||||
for data in dataset:
|
||||
d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
|
||||
d_datetime = datetime.combine(curr_date, d_time)
|
||||
|
||||
row = (device_id,
|
||||
data["value"],
|
||||
d_datetime,
|
||||
d_datetime.date(),
|
||||
d_datetime.month,
|
||||
d_datetime.day,
|
||||
d_datetime.weekday(),
|
||||
d_datetime.time(),
|
||||
d_datetime.hour,
|
||||
d_datetime.minute,
|
||||
HOUR2EPOCH[d_datetime.hour])
|
||||
|
||||
records.append(row)
|
||||
|
||||
return pd.DataFrame(data=records, columns=STEPS_COLUMNS)
|
||||
|
||||
def parse_sleep_data(sleep_data):
|
||||
if sleep_data.empty:
|
||||
return pd.DataFrame(columns=SLEEP_COLUMNS)
|
||||
device_id = sleep_data["device_id"].iloc[0]
|
||||
records = []
|
||||
# Parse JSON into individual records
|
||||
for multi_record in sleep_data.fitbit_data:
|
||||
for record in json.loads(multi_record)["sleep"]:
|
||||
|
||||
# Compute date when sleep episodes span two days
|
||||
start_date = datetime.strptime(record["startTime"][:10], "%Y-%m-%d")
|
||||
end_date = datetime.strptime(record["endTime"][:10], "%Y-%m-%d")
|
||||
flag = 1 if start_date == end_date else 0
|
||||
for data in record["minuteData"]:
|
||||
d_time = datetime.strptime(data["dateTime"], '%H:%M:%S').time()
|
||||
if not flag and not d_time.hour:
|
||||
flag = 1
|
||||
curr_date = end_date if flag else start_date
|
||||
d_datetime = datetime.combine(curr_date, d_time)
|
||||
|
||||
row = (device_id,
|
||||
data["value"],
|
||||
d_datetime,
|
||||
d_datetime.date(),
|
||||
d_datetime.month,
|
||||
d_datetime.day,
|
||||
d_datetime.weekday(),
|
||||
d_datetime.time(),
|
||||
d_datetime.hour,
|
||||
d_datetime.minute,
|
||||
HOUR2EPOCH[d_datetime.hour])
|
||||
|
||||
records.append(row)
|
||||
|
||||
return pd.DataFrame(data=records, columns=SLEEP_COLUMNS)
|
||||
|
||||
def parse_heartrate_data(heartrate_data):
|
||||
if heartrate_data.empty:
|
||||
return pd.DataFrame(columns=HR_COLUMNS)
|
||||
device_id = heartrate_data["device_id"].iloc[0]
|
||||
records = []
|
||||
|
||||
# Get the range of heartrate zones: outofrange, fatburn, cardio, peak
|
||||
# refer to: https://help.fitbit.com/articles/en_US/Help_article/1565
|
||||
|
||||
heartrate_fitbit_data = json.loads(heartrate_data["fitbit_data"].iloc[0])["activities-heart"][0]
|
||||
if "heartRateZones" in heartrate_fitbit_data:
|
||||
heartrate_zones = heartrate_fitbit_data["heartRateZones"]
|
||||
elif "value" in heartrate_fitbit_data:
|
||||
heartrate_zones = heartrate_fitbit_data["value"]["heartRateZones"]
|
||||
else:
|
||||
raise ValueError("Please check the format of fitbit heartrate raw data.")
|
||||
|
||||
heartrate_zones_range = {}
|
||||
for hrzone in heartrate_zones:
|
||||
heartrate_zones_range[hrzone["name"].lower().replace(" ", "")] = [hrzone["min"], hrzone["max"]]
|
||||
|
||||
# Parse JSON into individual records
|
||||
for record in heartrate_data.fitbit_data:
|
||||
record = json.loads(record) # Parse text into JSON
|
||||
curr_date = datetime.strptime(record["activities-heart"][0]["dateTime"], "%Y-%m-%d")
|
||||
dataset = record["activities-heart-intraday"]["dataset"]
|
||||
for data in dataset:
|
||||
d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
|
||||
d_datetime = datetime.combine(curr_date, d_time)
|
||||
d_hr = data["value"]
|
||||
|
||||
# Get heartrate zone by range: min <= heartrate < max
|
||||
d_hrzone = None
|
||||
for hrzone, hrrange in heartrate_zones_range.items():
|
||||
if d_hr >= hrrange[0] and d_hr < hrrange[1]:
|
||||
d_hrzone = hrzone
|
||||
break
|
||||
|
||||
row = (device_id,
|
||||
d_hr,
|
||||
d_hrzone,
|
||||
d_datetime,
|
||||
d_datetime.date(),
|
||||
d_datetime.month,
|
||||
d_datetime.day,
|
||||
d_datetime.weekday(),
|
||||
d_datetime.time(),
|
||||
d_datetime.hour,
|
||||
d_datetime.minute,
|
||||
HOUR2EPOCH[d_datetime.hour])
|
||||
|
||||
records.append(row)
|
||||
|
||||
return pd.DataFrame(data=records, columns=HR_COLUMNS)
|
||||
|
||||
def parse_calories_data(calories_data):
|
||||
if calories_data.empty:
|
||||
return pd.DataFrame(columns=CALORIES_COLUMNS)
|
||||
device_id = calories_data["device_id"].iloc[0]
|
||||
records = []
|
||||
# Parse JSON into individual records
|
||||
for record in calories_data.fitbit_data:
|
||||
record = json.loads(record) # Parse text into JSON
|
||||
curr_date = datetime.strptime(
|
||||
record["activities-calories"][0]["dateTime"], "%Y-%m-%d")
|
||||
dataset = record["activities-calories-intraday"]["dataset"]
|
||||
for data in dataset:
|
||||
d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
|
||||
d_datetime = datetime.combine(curr_date, d_time)
|
||||
|
||||
row = (device_id,
|
||||
data["level"],
|
||||
data["mets"],
|
||||
data["value"],
|
||||
d_datetime,
|
||||
d_datetime.date(),
|
||||
d_datetime.month,
|
||||
d_datetime.day,
|
||||
d_datetime.weekday(),
|
||||
d_datetime.time(),
|
||||
d_datetime.hour,
|
||||
d_datetime.minute,
|
||||
HOUR2EPOCH[d_datetime.hour])
|
||||
|
||||
records.append(row)
|
||||
|
||||
return pd.DataFrame(data=records, columns=CALORIES_COLUMNS)
|
||||
|
||||
|
||||
fitbit_data = pd.read_csv(snakemake.input[0])
|
||||
|
|
@ -211,14 +37,16 @@ data = fitbit_data[fitbit_data["fitbit_data_type"] == sensor]
|
|||
data = drop_duplicates(data, local_timezone)
|
||||
|
||||
if sensor == "heartrate":
|
||||
data_preprocesed = parse_heartrate_data(data)
|
||||
summary_data, intraday_data = parseHeartrateData(data, HOUR2EPOCH)
|
||||
elif sensor == "sleep":
|
||||
data_preprocesed = parse_sleep_data(data)
|
||||
summary_data, intraday_data = parseSleepData(data, HOUR2EPOCH)
|
||||
elif sensor == "steps":
|
||||
data_preprocesed = parse_steps_data(data)
|
||||
summary_data, intraday_data = parseStepsData(data, HOUR2EPOCH)
|
||||
elif sensor == "calories":
|
||||
data_preprocesed = parse_calories_data(data)
|
||||
summary_data, intraday_data = parseCaloriesData(data, HOUR2EPOCH)
|
||||
else:
|
||||
raise ValueError("Please check the FITBIT_SENSORS list in config.yaml file.")
|
||||
|
||||
data_preprocesed.to_csv(snakemake.output[0], index=False)
|
||||
# Summary data will be empty for steps and calories as it is not provided by Fitbit's API
|
||||
summary_data.to_csv(snakemake.output["summary_data"], index=False)
|
||||
intraday_data.to_csv(snakemake.output["intraday_data"], index=False)
|
||||
|
|
|
|||
|
|
@ -4,47 +4,75 @@ from scipy.stats import entropy
|
|||
import json
|
||||
|
||||
|
||||
heartrate_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
|
||||
def extractHRFeaturesFromSummaryData(heartrate_summary_data, daily_features_from_summary_data):
|
||||
heartrate_summary_features = pd.DataFrame()
|
||||
if "restinghr" in daily_features_from_summary_data:
|
||||
heartrate_summary_features["heartrate_daily_restinghr"] = heartrate_summary_data["heartrate_daily_restinghr"]
|
||||
# calories features might be inaccurate: they depend on users' fitbit profile (weight, height, etc.)
|
||||
if "caloriesoutofrange" in daily_features_from_summary_data:
|
||||
heartrate_summary_features["heartrate_daily_caloriesoutofrange"] = heartrate_summary_data["heartrate_daily_caloriesoutofrange"]
|
||||
if "caloriesfatburn" in daily_features_from_summary_data:
|
||||
heartrate_summary_features["heartrate_daily_caloriesfatburn"] = heartrate_summary_data["heartrate_daily_caloriesfatburn"]
|
||||
if "caloriescardio" in daily_features_from_summary_data:
|
||||
heartrate_summary_features["heartrate_daily_caloriescardio"] = heartrate_summary_data["heartrate_daily_caloriescardio"]
|
||||
if "caloriespeak" in daily_features_from_summary_data:
|
||||
heartrate_summary_features["heartrate_daily_caloriespeak"] = heartrate_summary_data["heartrate_daily_caloriespeak"]
|
||||
heartrate_summary_features.reset_index(inplace=True)
|
||||
|
||||
return heartrate_summary_features
|
||||
|
||||
def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features):
|
||||
heartrate_intraday_features = pd.DataFrame(columns=["local_date"] + ["heartrate_" + day_segment + "_" + x for x in features])
|
||||
if not heartrate_intraday_data.empty:
|
||||
device_id = heartrate_intraday_data["device_id"][0]
|
||||
num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"]
|
||||
if day_segment != "daily":
|
||||
heartrate_intraday_data = heartrate_intraday_data[heartrate_intraday_data["local_day_segment"] == day_segment]
|
||||
|
||||
if not heartrate_intraday_data.empty:
|
||||
heartrate_intraday_features = pd.DataFrame()
|
||||
|
||||
# get stats of heartrate
|
||||
if "maxhr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_maxhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].max()
|
||||
if "minhr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_minhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].min()
|
||||
if "avghr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_avghr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].mean()
|
||||
if "medianhr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_medianhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].median()
|
||||
if "modehr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_modehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0])
|
||||
if "stdhr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_stdhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].std()
|
||||
if "diffmaxmodehr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_diffmaxmodehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].max() - heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0])
|
||||
if "diffminmodehr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_diffminmodehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].min()
|
||||
if "entropyhr" in features:
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_entropyhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(entropy)
|
||||
|
||||
# get number of minutes in each heart rate zone
|
||||
for feature_name in list(set(["lengthoutofrange", "lengthfatburn", "lengthcardio", "lengthpeak"]) & set(features)):
|
||||
heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[6:]]
|
||||
heartrate_intraday_features["heartrate_" + day_segment + "_" + feature_name] = heartrate_zone.groupby(["local_date"])["device_id"].count() / num_rows_per_minute
|
||||
heartrate_intraday_features.fillna(value={"heartrate_" + day_segment + "_" + feature_name: 0}, inplace=True)
|
||||
heartrate_intraday_features.reset_index(inplace=True)
|
||||
|
||||
return heartrate_intraday_features
|
||||
|
||||
|
||||
heartrate_summary_data = pd.read_csv(snakemake.input["heartrate_summary_data"], index_col=["local_date"], parse_dates=["local_date"])
|
||||
heartrate_intraday_data = pd.read_csv(snakemake.input["heartrate_intraday_data"], parse_dates=["local_date_time", "local_date"])
|
||||
day_segment = snakemake.params["day_segment"]
|
||||
features = snakemake.params["features"]
|
||||
daily_features_from_summary_data = snakemake.params["daily_features_from_summary_data"]
|
||||
|
||||
|
||||
heartrate_features = pd.DataFrame(columns=["local_date"] + ["heartrate_" + day_segment + "_" + x for x in features])
|
||||
if not heartrate_data.empty:
|
||||
device_id = heartrate_data["device_id"][0]
|
||||
num_rows_per_minute = heartrate_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"]
|
||||
if day_segment != "daily":
|
||||
heartrate_data =heartrate_data[heartrate_data["local_day_segment"] == day_segment]
|
||||
|
||||
if not heartrate_data.empty:
|
||||
heartrate_features = pd.DataFrame()
|
||||
|
||||
# get stats of heartrate
|
||||
if "maxhr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_maxhr"] = heartrate_data.groupby(["local_date"])["heartrate"].max()
|
||||
if "minhr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_minhr"] = heartrate_data.groupby(["local_date"])["heartrate"].min()
|
||||
if "avghr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_avghr"] = heartrate_data.groupby(["local_date"])["heartrate"].mean()
|
||||
if "medianhr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_medianhr"] = heartrate_data.groupby(["local_date"])["heartrate"].median()
|
||||
if "modehr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_modehr"] = heartrate_data.groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0])
|
||||
if "stdhr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_stdhr"] = heartrate_data.groupby(["local_date"])["heartrate"].std()
|
||||
if "diffmaxmodehr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_diffmaxmodehr"] = heartrate_data.groupby(["local_date"])["heartrate"].max() - heartrate_data.groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0])
|
||||
if "diffminmodehr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_diffminmodehr"] = heartrate_data.groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data.groupby(["local_date"])["heartrate"].min()
|
||||
if "entropyhr" in features:
|
||||
heartrate_features["heartrate_" + day_segment + "_entropyhr"] = heartrate_data.groupby(["local_date"])["heartrate"].agg(entropy)
|
||||
|
||||
# get number of minutes in each heart rate zone
|
||||
for feature_name in list(set(["lengthoutofrange", "lengthfatburn", "lengthcardio", "lengthpeak"]) & set(features)):
|
||||
heartrate_zone = heartrate_data[heartrate_data["heartrate_zone"] == feature_name[6:]]
|
||||
heartrate_features["heartrate_" + day_segment + "_" + feature_name] = heartrate_zone.groupby(["local_date"])["device_id"].count() / num_rows_per_minute
|
||||
heartrate_features.fillna(value={"heartrate_" + day_segment + "_" + feature_name: 0}, inplace=True)
|
||||
|
||||
heartrate_features = heartrate_features.reset_index()
|
||||
heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, features)
|
||||
if not heartrate_summary_data.empty and day_segment == "daily" and daily_features_from_summary_data != []:
|
||||
heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, daily_features_from_summary_data)
|
||||
heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_date"], how="outer")
|
||||
else:
|
||||
heartrate_features = heartrate_intraday_features
|
||||
|
||||
heartrate_features.to_csv(snakemake.output[0], index=False)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,67 @@
|
|||
import pandas as pd
|
||||
import itertools
|
||||
|
||||
|
||||
|
||||
def dailyFeaturesFromSummaryData(sleep_summary_data, sleep_type):
|
||||
if sleep_type == "main":
|
||||
sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1]
|
||||
elif sleep_type == "nap":
|
||||
sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0]
|
||||
elif sleep_type == "all":
|
||||
pass
|
||||
else:
|
||||
raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].")
|
||||
|
||||
features_sum = sleep_summary_data[["minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "local_end_date"]].groupby(["local_end_date"]).sum()
|
||||
features_sum.index.rename("local_date", inplace=True)
|
||||
if "sumdurationafterwakeup" in daily_features_from_summary_data:
|
||||
sleep_daily_features["sleep_daily_sumdurationafterwakeup" + sleep_type] = features_sum["minutes_after_wakeup"]
|
||||
if "sumdurationasleep" in daily_features_from_summary_data:
|
||||
sleep_daily_features["sleep_daily_sumdurationasleep" + sleep_type] = features_sum["minutes_asleep"]
|
||||
if "sumdurationawake" in daily_features_from_summary_data:
|
||||
sleep_daily_features["sleep_daily_sumdurationawake" + sleep_type] = features_sum["minutes_awake"]
|
||||
if "sumdurationtofallasleep" in daily_features_from_summary_data:
|
||||
sleep_daily_features["sleep_daily_sumdurationtofallasleep" + sleep_type] = features_sum["minutes_to_fall_asleep"]
|
||||
if "sumdurationinbed" in daily_features_from_summary_data:
|
||||
sleep_daily_features["sleep_daily_sumdurationinbed" + sleep_type] = features_sum["minutes_in_bed"]
|
||||
|
||||
features_avg = sleep_summary_data[["efficiency", "local_end_date"]].groupby(["local_end_date"]).mean()
|
||||
features_avg.index.rename("local_date", inplace=True)
|
||||
if "avgefficiency" in daily_features_from_summary_data:
|
||||
sleep_daily_features["sleep_daily_avgefficiency" + sleep_type] = features_avg["efficiency"]
|
||||
|
||||
features_count = sleep_summary_data[["local_start_date_time", "local_end_date"]].groupby(["local_end_date"]).count()
|
||||
features_count.index.rename("local_date", inplace=True)
|
||||
if "countepisode" in daily_features_from_summary_data:
|
||||
sleep_daily_features["sleep_daily_count" + sleep_type] = features_count["local_start_date_time"]
|
||||
|
||||
return sleep_daily_features
|
||||
|
||||
|
||||
|
||||
sleep_summary_data = pd.read_csv(snakemake.input["sleep_summary_data"])
|
||||
sleep_types = snakemake.params["sleep_types"]
|
||||
daily_features_from_summary_data = snakemake.params["daily_features_from_summary_data"]
|
||||
day_segment = snakemake.params["day_segment"]
|
||||
|
||||
daily_features_can_be_zero = list(set(daily_features_from_summary_data) - set(["avgefficiency"]))
|
||||
colnames_can_be_zero = ["sleep_daily_" + x for x in ["".join(feature) for feature in itertools.product(daily_features_can_be_zero, sleep_types)]]
|
||||
|
||||
colnames = ["sleep_daily_" + x for x in ["".join(feature) for feature in itertools.product(daily_features_from_summary_data, sleep_types)]]
|
||||
|
||||
if sleep_summary_data.empty:
|
||||
sleep_daily_features = pd.DataFrame(columns=["local_date"] + colnames)
|
||||
else:
|
||||
sleep_daily_features = pd.DataFrame(columns=colnames)
|
||||
for sleep_type in sleep_types:
|
||||
sleep_daily_features = dailyFeaturesFromSummaryData(sleep_summary_data, sleep_type)
|
||||
|
||||
sleep_daily_features[colnames_can_be_zero] = sleep_daily_features[colnames_can_be_zero].fillna(0)
|
||||
|
||||
|
||||
|
||||
if day_segment == "daily":
|
||||
sleep_daily_features.to_csv(snakemake.output[0])
|
||||
else:
|
||||
ValueError("Sleep summary features are only implemented for daily day segments")
|
||||
Loading…
Reference in New Issue